The open Kubernetes ecosystem provides several powerful tools to help manage resources effectively, including the Horizontal Pod Autoscaler and Vertical Pod Autoscaler, but implementing these tools requires significant effort from platform and application teams. Datadog Kubernetes Autoscaling provides multi-dimensional workload scaling recommendations and automation, enabling teams to deliver cost savings while maintaining performance and stability. This tool helps prioritize clusters and workloads for optimization by surfacing idle resources and providing time-series graphs of recent cost trends. Once a cluster is targeted, the tool enables teams to rightsize workloads directly within Datadog, providing complete recommendations and the ability to take action automatically or enable automation. By tracking progress over time, teams can track efficiency gains and idle cost savings, making it easier to optimize their Kubernetes clusters and save costs without sacrificing performance.